Markers for risk to develop atrial fibrillation based on a new electrocardiographic tool

Atrial fibrillation (AF) is the most important trigger for heart failure and stroke. Reliable methods for predicting AF are lacking and ECG analysis could improve this process. P-wave characteristics in the ECG are an important source of information in the diagnosis of atrial conduction pathology. In our study, we investigate different markers resulting from ECG analysis for detecting AF early stages. Methods: our database consists of 48 ECG from healthy patients (group N, age: 45 to 75 y) , 5 ECG from healthy patients (group B, age: 75 to 85 y) and a third group of 17 ECG from patients with AF history (group AF, age: 48 to 72 y). All the measurements were acquired in sinus rhythm. The ECG recording duration for each patient was of 5 minutes (sampling frequency: 1 KHz). Pre-processing was applied in order to reduce the noise and baseline wander. The P-wave was detected on lead V1 which provided information concerning intra-atrial conduction delays. Several P-wave characteristics were extracted: 1) P width, 2) P-R interval for all beats and 3) the variance of the beat-to-beat P-wave Euclidean distance, measuring P-wave time stability. Results: no significant differences between the groups were obtained using the P-R interval (100±33 ms, 89±41 ms, 106±38 ms, p=ns) and using the P width (120±18 ms, 111±19 ms, 124±24 ms, p=ns). The variance of the beat-to-beat P-wave Euclidean distance was higher for the AF group as compared to N and B groups (0.23±0.12, 0.26±0.26, 0.46±0.32, p<0.001). Conclusions: the Euclidean distance appears as a promising marker for the recognition of patients prone to AF, but it should be combined with other markers in order to improve patient classification.